One-hot encoding refers to a way of transforming data into vectors where all components are 0, except for one component with a value of 1, e,g.: \[ 0 = [1, 0, 0, 0, 0]^T \] \[ 1 = [0, 1, 0, 0, 0]^T \] \[ \ldots \] \[ 4 = [0, 0, 0, 0, 1]^T \] and so on.

One-hot encoding can make it easier for machine learning algorithms to manipulate and learn categorical variables.